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      Weak evidence for fine-scale genetic spatial structure in three sedentary Amazonian understorey birds

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          Preservation of avian blood and tissue samples for DNA analyses

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            Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure.

            Population genetic theory predicts that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricted, but as an empirical reality, spatial structure is rarely consistent across loci or sites, and is generally weak. A lack of sensitivity in the statistical procedures may explain the discrepancy. Most work to date, based on allozymes, has involved pattern analysis for individual alleles, but new PCR-based genetic markers are coming into vogue, with vastly increased numbers of alleles. The field is badly in need of an explicitly multivariate approach to autocorrelation analysis, and our purpose here is to introduce a new approach that is applicable to multiallelic codominant, multilocus arrays. The procedure treats the genetic data set as a whole, strengthening the spatial signal and reducing the stochastic (allele-to-allele, and locus-to-locus) noise. We (i) develop a very general multivariate method, based on genetic distance methods, (ii) illustrate it for multiallelic codominant loci, and (iii) provide nonparametric permutational testing procedures for the full correlogram. We illustrate the new method with an example data set from the orchid Caladenia tentaculata, for which we show (iv) how the multivariate treatment compares with the single-allele treatment, (v) that intermediate frequency alleles from highly polymorphic loci perform well and rare alleles poorly, (vi) that a multilocus treatment provides clearer answers than separate single-locus treatments, and (vii) that weighting alleles differentially improves our resolution minimally. The results, though specific to Caladenia, offer encouragement for wider application.
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              The drivers of tropical speciation.

              Since the recognition that allopatric speciation can be induced by large-scale reconfigurations of the landscape that isolate formerly continuous populations, such as the separation of continents by plate tectonics, the uplift of mountains or the formation of large rivers, landscape change has been viewed as a primary driver of biological diversification. This process is referred to in biogeography as vicariance. In the most species-rich region of the world, the Neotropics, the sundering of populations associated with the Andean uplift is ascribed this principal role in speciation. An alternative model posits that rather than being directly linked to landscape change, allopatric speciation is initiated to a greater extent by dispersal events, with the principal drivers of speciation being organism-specific abilities to persist and disperse in the landscape. Landscape change is not a necessity for speciation in this model. Here we show that spatial and temporal patterns of genetic differentiation in Neotropical birds are highly discordant across lineages and are not reconcilable with a model linking speciation solely to landscape change. Instead, the strongest predictors of speciation are the amount of time a lineage has persisted in the landscape and the ability of birds to move through the landscape matrix. These results, augmented by the observation that most species-level diversity originated after episodes of major Andean uplift in the Neogene period, suggest that dispersal and differentiation on a matrix previously shaped by large-scale landscape events was a major driver of avian speciation in lowland Neotropical rainforests.
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                Author and article information

                Journal
                Journal of Ornithology
                J Ornithol
                Springer Science and Business Media LLC
                2193-7192
                2193-7206
                April 2018
                November 3 2017
                April 2018
                : 159
                : 2
                : 355-366
                Article
                10.1007/s10336-017-1507-y
                9af714dc-4af6-482e-a0be-43fdae48e7d5
                © 2018

                http://www.springer.com/tdm

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